Data Analytics: You’ll Need It During Supply Chain Disruptions

From congested ports and labor shortages to generational droughts in the Suez Canal and extended geopolitical conflicts, the efficiency we once took for granted has evaporated, forcing freight, logistics, and supply chain organizations to turn to advanced technology for solutions. 

Faced with these challenges, real-time data analytics has emerged as a critical tool for building visibility in supply chain operations. A McKinsey survey found companies with end-to-end visibility (powered by data analytics) were twice as likely to avoid disruptions in 2022. 

But before we explore where and how data analytics should be deployed in the supply chain to harness its benefits fully, we need to address the fragmented data landscape and lack of accessibility that prevent its widespread adoption in supply chain strategies. Overcoming these barriers is crucial for organizations seeking to build the cognitive supply chains of the future.

Uncovering the root of supply chain inefficiencies 

"The typical supply chain in 2018 accessed 50 times more data than just five years earlier," explains Jesus Mantas from IBM Global Business Services. "Yet less than a quarter of it is analyzed in real-time. This underscores a massive opportunity to use data across incredibly complex, global operations – from shipment tracking and weather patterns to supplier metrics and IoT sensor insights.”

The real problem isn't the amount of data. It's how it's trapped. Data silos and messy formats create a fragmented landscape that limits visibility and causes inefficiencies across the board.  But imagine unlocking that trapped unstructured data into a goldmine of insights to drive real optimization. That's the power of data analytics.

The power of data: how analytics transforms your supply chain

Data analytics brings much-needed visibility to supply chain planning and optimization. Organizations can identify inefficiencies, optimize inventory levels, improve transportation routes, and transform previously unusable data into actionable insights by leveraging data from various sources, such as procurement, logistics, and customer demand.

How data analytics works

• Data Collection: Aggregates data from diverse sources, including IoT devices, ERP systems and company inboxes.
• Data Cleansing: Filters out inaccuracies to ensure analyses are based on quality data.
• Data Structuring: Organizes unstructured data into a structured format for more accessible analysis.
• Data Analysis: Utilizes statistical models and machine learning to uncover patterns.
• Insight Generation: Translates findings into actionable insights for decision-making.

Data analytics also facilitates collaboration and transparency across the supply chain network, empowering organizations to optimize processes, reduce costs, mitigate risks, and enhance customer satisfaction.

Data analytics provides immense value in optimizing supply chain operations alone. However, combined with AI, it enables a previously unattainable level of insight and automation. Here's how. 

AI unlocks the full potential of data analytics

Let’s say an international freight forwarder is grappling with port congestion and escalating shipping costs in the Suez Canal. The delays are disrupting customer schedules, causing lost sales, and eroding profit margins. The company desperately needs insights to optimize routes, minimize delays, and protect its bottom line.

How does this help freight forwarders?

For freight forwarders grappling with the Red Sea turmoil, traditional strategies are no longer sufficient. AI-enhanced data analytics means you can shift gears by analyzing the overwhelming data to unlock insights that can protect your margins during these disruptions. 

Optimal route mapping: AI algorithms crunch real-time data on port congestion, vessel tracking, security threats, and historical incidents. They pinpoint lower-risk alternative routes, even unconventional ones while calculating cost-delay tradeoffs. That means being able to help clients with the optimal routings to maximize on-time deliveries.

Early warning system: By analyzing news feeds, social posts, and maritime activity, AI can predict potential flare-ups before they hit your shipments, allowing forwarders to practice proactive rerouting or carrier changes to avoid those costly penalties.

Carrier performance analysis: AI evaluates how carriers perform under duress in the Red Sea, delivering intelligence to negotiate favorable contracts with reliable partners and the agility to switch providers if needed.

Customer trust: AI-driven visibility into changing Red Sea conditions allows forwarders to communicate proactively with customers on delays and rerouting. This transparency builds trust, minimizes costly chargebacks, and justifies surcharges when warranted.

Turn your data into 20% more annual revenue. 

At Stargo, we've integrated GenAI into our Stardox platform, which is tailored to the freight, logistics, supply chain, and marine insurance industries. With Stardox, supply chain organizations can automatically structure, correct, and enrich their data, creating a single source of truth for end-to-end visibility and optimization. Schedule a demo to see how Stardox uncovers 20% more in annual revenue from your current data management strategy in as little as with results within 12 weeks.